Smart Grid Challenges Through the Lens of the European General Data Protection Regulation

Martínez, Jabier; Ruíz, Alejandra; Puelles, Javier; Arechalde, Ibon; Miadzvetskaya, Yuliya

doi:10.1007/978-3-030-49644-9_7

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…This can be found in the General Data Protection Regulation (GDPR) that ensures the protection of natural persons with regard to the processing of their personal data and guarantees the free movement of such data, provided that the appropriate safeguards are applied. A review of the existing works highlighting the importance of energy consumption as valuable personal data and its main challenges from a legal viewpoint is presented in [82]. In addition, aggregators must protect their business processes from external cyber attacks by implementing efficient IT security controls, as discussed for instance by Lu et al [8].…”

Section: Discussion Of Factors Affecting Aggregators' Development And...mentioning

confidence: 99%

Integrated Risk Analysis of Aggregators: Policy Implications for the Development of the Competitive Aggregator Industry

et al. 2022

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One of the important goals of the EU is to ensure a secure, sustainable, and competitive energy system that is less dependent on external energy supply. Greater independence is planned to be achieved by diversifying energy sources, as well as investing in renewables and energy efficiency. One of the mechanisms is the demand response (DR) that provides a high level of energy independence for the consumer. In this paper, we explore perspectives of the development of DR with a mediating effect of the independent aggregators from an EU member state standpoint. We use a hybrid research methodology that combines instruments of strategic analysis, i.e., PESTLE framework and SWOT analysis, along with the integrated risk management framework in order to identify, evaluate and rank prominent risks to which this initiative is exposed. Interdependencies between the identified risk factors are also included and efficient mitigation measures are proposed. The findings of this exploratory research are aimed at developing the policies and strategies for the aggregators’ development in the medium term. The most emphasized risks detected in analysis are the investment risk, the legal risk, the risk of substitute technologies, the consumer behavior risk, the risk of opportunistic behavior and the risk of entry barriers.

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Section: Discussion Of Factors Affecting Aggregators' Development And...mentioning

confidence: 99%

Integrated Risk Analysis of Aggregators: Policy Implications for the Development of the Competitive Aggregator Industry

et al. 2022

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“…As a result, the potential privacy implications resulting from smart meter usage should not be underestimated. One of the main challenges of smart meters and their broader ecosystem design, is the sharing of data with third parties, such as device manufacturers and for highly personalised marketing campaigns (Martinez et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

AI and privacy concerns: a smart meter case study

Carmody

Shringarpure

Venter

2021

JICES

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Purpose The purpose of this paper is to demonstrate privacy concerns arising from the rapidly increasing advancements and use of artificial intelligence (AI) technology and the challenges of existing privacy regimes to ensure the on-going protection of an individual’s sensitive private information. The authors illustrate this through a case study of energy smart meters and suggest a novel combination of four solutions to strengthen privacy protection. Design/methodology/approach The authors illustrate how, through smart meter obtained energy data, home energy providers can use AI to reveal private consumer information such as households’ electrical appliances, their time and frequency of usage, including number and model of appliance. The authors show how this data can further be combined with other data to infer sensitive personal information such as lifestyle and household income due to advances in AI technologies. Findings The authors highlight data protection and privacy concerns which are not immediately obvious to consumers due to the capabilities of advanced AI technology and its ability to extract sensitive personal information when applied to large overlapping granular data sets. Social implications The authors question the adequacy of existing privacy legislation to protect sensitive inferred consumer data from AI-driven technology. To address this, the authors suggest alternative solutions. Originality/value The original value of this paper is that it illustrates new privacy issues brought about by advances in AI, failings in current privacy legislation and implementation and opens the dialog between stakeholders to protect vulnerable consumers.

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“…DSO companies seeking to enhance grid maintenance levels must comprehend and adhere to the GDPR requirements to ensure the lawful and responsible processing of personal data. Seeking guidance from legal professionals or data protection authorities can provide further assistance in fulfilling specific compliance obligations [156,157].…”

Section: General Data Protection Regulationmentioning

confidence: 99%

A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids

Mirshekali,

Santos,

Shaker

2023

Energies

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The maintenance of electrical grids is crucial for improving their reliability, performance, and cost-effectiveness. It involves employing various strategies to ensure smooth operation and address potential issues. With the advancement of digital technologies, utilizing time-series prediction has emerged as a valuable approach to enhance maintenance practices in electrical systems. The utilization of various recorded data from electrical grid components plays a crucial role in digitally enabled maintenance. However, the comprehensive exploration of time-series data prediction for maintenance is still lacking. This review paper extensively explores different time series that can be utilized to support maintenance efforts in electrical grids with regard to different maintenance strategies and grid components. The digitization of the electrical grids has enabled the collection of diverse time-series data from various network components. In this context, the paper provides an overview of how these time-series and historical-fault data can be utilized for maintenance purposes in electrical grids. Various maintenance levels and time series used for maintenance purposes in different components of the electrical grid are presented.

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Smart Grid Challenges Through the Lens of the European General Data Protection Regulation

Cited by 8 publications

References 31 publications

Integrated Risk Analysis of Aggregators: Policy Implications for the Development of the Competitive Aggregator Industry

Integrated Risk Analysis of Aggregators: Policy Implications for the Development of the Competitive Aggregator Industry

AI and privacy concerns: a smart meter case study

A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids

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